import pandas as pd

# Create Series from a list
s = pd.Series([10, 20, 30, 40, 50])
print(s)
# Output:
# 0    10
# 1    20
# 2    30
# 3    40
# 4    50
# dtype: int64

# Create Series from a dictionary (keys become labels)
data = {'a': 100, 'b': 200, 'c': 300}
s = pd.Series(data)
print(s)
# Output:
# a    100
# b    200
# c    300
# dtype: int64

import matplotlib.pyplot as plt

months = ['Jan', 'Feb', 'Mar', 
          'Apr', 'May', 'Jun']
sales = [15000, 18000, 16500, 
         21000, 23500, 25000]

plt.figure(figsize=(8, 5))
plt.plot(months, sales, 
         marker='o', 
         linewidth=2, 
         color='blue', 
         label='Sales')
plt.xlabel('Month')
plt.ylabel('Sales ($)')
plt.title('Monthly Sales Trend')
plt.grid(True, alpha=0.3)
plt.legend()
plt.show()

import matplotlib.pyplot as plt
import numpy as np

# Sample data: study hours vs exam scores
study_hours = [1, 2, 3, 4, 5, 6, 7, 8]
exam_scores = [50, 55, 60, 65, 75, 80, 85, 90]

plt.figure(figsize=(8, 5))
plt.scatter(study_hours, exam_scores, 
            s=100, color='green', 
            alpha=0.6, edgecolors='black')
plt.xlabel('Study Hours')
plt.ylabel('Exam Score')
plt.title('Study Hours vs Exam Score')
plt.grid(True, alpha=0.3)
plt.show()

import matplotlib.pyplot as plt

# Sample data: product sales by category
categories = ['Electronics', 'Clothing', 
              'Food', 'Books', 'Toys']
sales = [45000, 32000, 28000, 18000, 15000]

plt.figure(figsize=(8, 5))
plt.bar(categories, sales, 
        color=['#FF6B6B', '#4ECDC4', 
               '#45B7D1', '#FFA07A', '#98D8C8'])
plt.xlabel('Product Category')
plt.ylabel('Sales ($)')
plt.title('Sales by Product Category')
plt.xticks(rotation=45)
plt.tight_layout()
plt.show()

import matplotlib.pyplot as plt
import numpy as np

# Sample data: student ages in a class
ages = np.random.normal(20, 2, 100)

plt.figure(figsize=(8, 5))
plt.hist(ages, bins=15, color='purple', 
         alpha=0.7, edgecolor='black')
plt.xlabel('Age')
plt.ylabel('Frequency')
plt.title('Distribution of Student Ages')
plt.grid(True, alpha=0.3, axis='y')
plt.show()

import matplotlib.pyplot as plt

# Sample data: budget allocation
categories = ['Marketing', 'R&D', 
              'Operations', 'HR', 'IT']
budget = [30, 25, 20, 15, 10]
colors = ['#FF9999', '#66B2FF', '#99FF99', 
          '#FFCC99', '#FF99CC']

plt.figure(figsize=(8, 6))
plt.pie(budget, labels=categories, 
        autopct='%1.1f%%', 
        startangle=90, colors=colors)
plt.title('Department Budget Allocation')
plt.axis('equal')
plt.show()

import matplotlib.pyplot as plt
import numpy as np

# Sample data: test scores
class_a = np.random.normal(75, 10, 50)
class_b = np.random.normal(80, 8, 50)
class_c = np.random.normal(70, 12, 50)

data = [class_a, class_b, class_c]

plt.figure(figsize=(8, 5))
plt.boxplot(data, 
            labels=['Class A', 'Class B', 
                    'Class C'], 
            patch_artist=True)
plt.ylabel('Test Scores')
plt.title('Test Score Distribution by Class')
plt.grid(True, alpha=0.3, axis='y')
plt.show()

import matplotlib.pyplot as plt
import numpy as np

# Sample data: correlation matrix
data = np.random.rand(5, 5)
labels = ['Math', 'Science', 'English', 
          'History', 'Art']

plt.figure(figsize=(8, 6))
plt.imshow(data, cmap='YlOrRd', 
           aspect='auto')
plt.colorbar(label='Correlation')
plt.xticks(range(5), labels, rotation=45)
plt.yticks(range(5), labels)
plt.title('Subject Correlation Heatmap')
plt.tight_layout()
plt.show()

import matplotlib.pyplot as plt
import numpy as np

# Sample data
x = np.linspace(0, 10, 100)

fig, axes = plt.subplots(2, 2, 
                         figsize=(10, 8))

# Plot 1: Line
axes[0, 0].plot(x, np.sin(x), 'b-')
axes[0, 0].set_title('Sine Wave')

# Plot 2: Scatter
axes[0, 1].scatter(x, np.cos(x), 
                   c='red', alpha=0.5)
axes[0, 1].set_title('Cosine Scatter')

# Plot 3: Bar
axes[1, 0].bar(['A', 'B', 'C'], [3, 7, 5])
axes[1, 0].set_title('Bar Chart')

# Plot 4: Histogram
axes[1, 1].hist(np.random.randn(1000), 
                bins=30, color='green', 
                alpha=0.7)
axes[1, 1].set_title('Histogram')

plt.tight_layout()
plt.show()

import seaborn as sns
import matplotlib.pyplot as plt

# Available styles:
# 'darkgrid', 'whitegrid', 'dark', 
# 'white', 'ticks'

# Set style
sns.set_style("whitegrid")

# Set context for scaling
# 'paper', 'notebook', 'talk', 'poster'
sns.set_context("talk")

# Set color palette
sns.set_palette("husl")

import seaborn as sns
import matplotlib.pyplot as plt

# Qualitative palettes (categorical)
sns.color_palette("Set2")
sns.color_palette("Paired")

# Sequential palettes (continuous)
sns.color_palette("Blues")
sns.color_palette("rocket")

# Diverging palettes (two extremes)
sns.color_palette("coolwarm")
sns.color_palette("vlag")

# Custom palette
custom = ["#FF6B6B", "#4ECDC4", "#45B7D1"]
sns.set_palette(custom)

import seaborn as sns
import matplotlib.pyplot as plt
import numpy as np

# Sample data
np.random.seed(42)
data = np.random.normal(100, 15, 1000)

# Create distribution plot
plt.figure(figsize=(10, 6))
sns.histplot(data, kde=True, 
             color='skyblue', bins=30)
plt.title('Distribution Plot with KDE')
plt.xlabel('Values')
plt.ylabel('Frequency')
plt.show()

import seaborn as sns
import matplotlib.pyplot as plt
import pandas as pd

# Sample data
df = pd.DataFrame({
    'Category': ['A']*50 + ['B']*50 + ['C']*50,
    'Values': np.concatenate([
        np.random.normal(20, 5, 50),
        np.random.normal(30, 7, 50),
        np.random.normal(25, 4, 50)
    ])
})

plt.figure(figsize=(10, 6))
sns.boxplot(data=df, x='Category', 
            y='Values', palette='Set2')
plt.title('Box Plot Comparison')
plt.show()

import seaborn as sns
import matplotlib.pyplot as plt

# Using same data from previous slide
plt.figure(figsize=(10, 6))
sns.violinplot(data=df, x='Category', 
               y='Values', palette='muted')
plt.title('Violin Plot - Distribution Shape')
plt.show()

import seaborn as sns
import matplotlib.pyplot as plt
import pandas as pd

# Sample data
df = pd.DataFrame({
    'Category': ['A', 'B', 'C', 'D', 'E'],
    'Mean': [23, 45, 32, 38, 41],
    'StdDev': [3, 5, 4, 3, 6]
})

plt.figure(figsize=(10, 6))
sns.barplot(data=df, x='Category', 
            y='Mean', palette='viridis',
            errorbar='sd')
plt.title('Bar Plot with Error Bars')
plt.ylabel('Average Value')
plt.show()

import seaborn as sns
import matplotlib.pyplot as plt
import pandas as pd

# Sample data
df = pd.DataFrame({
    'Category': ['A']*45 + ['B']*32 + 
                ['C']*28 + ['D']*15 + ['E']*23
})

plt.figure(figsize=(10, 6))
sns.countplot(data=df, x='Category', 
              palette='pastel')
plt.title('Count Plot - Frequency Distribution')
plt.ylabel('Count')
plt.show()

import seaborn as sns
import matplotlib.pyplot as plt
import numpy as np

# Sample data
np.random.seed(42)
x = np.random.rand(100) * 100
y = 2 * x + np.random.randn(100) * 15

plt.figure(figsize=(10, 6))
sns.regplot(x=x, y=y, 
            scatter_kws={'alpha':0.5},
            line_kws={'color':'red'})
plt.title('Scatter Plot with Regression Line')
plt.xlabel('X Variable')
plt.ylabel('Y Variable')
plt.show()

import seaborn as sns
import matplotlib.pyplot as plt
import numpy as np

# Sample correlation matrix
np.random.seed(42)
data = np.random.rand(5, 5)
labels = ['A', 'B', 'C', 'D', 'E']

plt.figure(figsize=(10, 8))
sns.heatmap(data, annot=True, fmt='.2f',
            cmap='coolwarm', 
            xticklabels=labels,
            yticklabels=labels,
            cbar_kws={'label': 'Correlation'})
plt.title('Correlation Heatmap')
plt.show()

import seaborn as sns
import matplotlib.pyplot as plt
from sklearn.datasets import load_iris

# Load iris dataset
iris = load_iris(as_frame=True)
df = iris.frame
df['species'] = df['target'].map({
    0: 'setosa', 
    1: 'versicolor', 
    2: 'virginica'
})

# Create pair plot
sns.pairplot(df, hue='species', 
             palette='Set1')
plt.show()

import seaborn as sns
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np

# Sample data
np.random.seed(42)
df = pd.DataFrame({
    'x': np.random.rand(300) * 100,
    'y': np.random.rand(300) * 100,
    'category': np.repeat(['A', 'B', 'C'], 100)
})

# Create FacetGrid
g = sns.FacetGrid(df, col='category', 
                  height=4)
g.map(sns.scatterplot, 'x', 'y')
g.add_legend()
plt.show()

import seaborn as sns
import matplotlib.pyplot as plt
import numpy as np

# Sample data
np.random.seed(42)
x = np.random.randn(500)
y = x + np.random.randn(500) * 0.5

# Create joint plot
sns.jointplot(x=x, y=y, kind='scatter',
              marginal_kws={'bins': 30,
                           'color': 'skyblue'},
              joint_kws={'alpha': 0.5})
plt.show()